Brain-state mediated modulation of inter-laminar dependencies in visual cortex

Spatial attention is critical for recognizing behaviorally relevant objects in a cluttered environment. How the deployment of spatial attention aids the hierarchical computations of object recognition remains unclear. We investigated this in the laminar cortical network of visual area V4, an area strongly modulated by attention. We found that deployment of attention strengthened unique dependencies in neural activity across cortical layers. On the other hand, shared dependencies were reduced within the excitatory population of a layer. Surprisingly, attention strengthened unique dependencies within a laminar population. Crucially, these modulation patterns were also observed during successful behavioral outcomes that are thought to be mediated by internal brain state fluctuations. Successful behavioral outcomes were also associated with phases of reduced neural excitability, suggesting a mechanism for enhanced information transfer during optimal states. Our results suggest common computation goals of optimal sensory states that are attained by either task demands or internal fluctuations.


BROAD CONCERN:
There is a key division of perspecfive in the literature on aftenfion in non-human primates that shows up here in various forms but is not directly addressed by the authors.On the one hand, starfing with the seminal studies in mid 1980s by Desimone and colleagues (including Reynolds et al) and confinuing with the studies of Maunsell and colleagues, invesfigators have taken the perspecfive that an essenfial component of aftending is suppression of low frequency excitability fluctuafions (aka oscillafions) and possibly enhancement of higher frequency (e.g., gamma).On the other hand, there is gathering recognifion that the lower frequencies persist during aftenfive states and are mechanisfic instruments in aftenfional modulafion of neuronal acfivity in the visual system.Relevant studies in both NHPs and humans include those of the Kastner group and the Knight group (cited here, refs 15-17) concerning the role of alpha/theta range oscillafions in spafial channel switching during sustained aftenfion), and other studies by Lakatos and colleagues (and others not cited here) showing that aftenfion uses low frequency (theta-delta range) entrainment to aid in selecfion of task relevant input at the expense of distractor input.The reason that this is important is that findings under the "low frequency suppression" framework generally entail random presentafion of sfimuli, while those in the "low frequency entrainment" framework perspecfive entail rhythmic or quasi-rhythmic input presentafion.Reynolds and colleagues (from whose lab the empirical data for this study were drawn) make a strong case (as may be obvious in the foregoing discussion) that low frequency dynamics are more "broad-band" than "narrowband" in character.I would agree with that.
Are these considerafions relevant here?I would say yes for several reasons.First, it is likely that aftenfion in the real-world alternates between random and rhythmic (and perhaps mixed) modes of operafion (e.g., Schroeder & Lakatos, 2009;Schroeder et al., 2010), parficularly since real world vision requires acfive saccadic sampling of informafion (Barczak et al., 2019); this is arguably the overt (motor) equivalent of covert spafial aftenfion.Saccades occur quasi-rhythmically at rates of 2-5 Hz, which strongly entrains visual pathway acfivity at these (delta-theta) rates.Second, enhancement of informafion flow across layers and across areas likely requires synchronizafion of acfivity at whatever fluctuafion/oscillafion rate is dominant.Third, the very presence of fluctuafions/oscillafions, however bursty/transient and on whatever fime scale (Neymofin et al., 2022;Tal et al., 2022) raises a conundrum that the authors would need to address if their proposed computafional model is to have general relevance for the mechanisfic study of aftenfion: the high excitability phase is where acfion potenfial most often occur, and synchronisafion of high excitability states x layers and/or areas is one of the more obvious ways to enhance informafion flow, HOWEVER, the high excitability phase is where one would expect "noise correlafions" to be the strongest.This could mean, for e.g., that the low excitability phase is paradoxically the best state for transmission of "unique" informafion (see point 4 below).In any case, I am missing in this paper is a considerafion of the impact of neuronal dynamics, parficularly in the lower (<30 Hz) frequencies.Given that as menfioned above, real world visual processing (in monkeys and humans) usually entails moving the eyes a lot, with the aftendant impact on neuronal dynamics, failure to fully consider dynamics and fime may limit the generality of this otherwise fine work (again, see point 4).

MORE SPECIFIC CONCERNS:
1) It would be really helpful to define "unique informafion" and "unique dependencies" early in the paper.
2) The term "fluctuafions" seems to correspond to neuronal "dynamics" or "oscillafions" but it would be befter to clear this up early rather than leaving the reader puzzling over this.
3) p2, top: do "laminar" stages (here middle to upper layers of V4) really represent "cross-areal" stages?I'm prepared to buy that for the moment, but one problem here that laminar projecfion pafterns from Layer 4 to Layers 2/3 are biased in favor of the "feedforward" (layer 4  3) projecfion, whereas corficocorfical projecfions tend more towards symmentry.It seems that the analyses here assume a ~ symmetrical 4  2/3 projecfion paftern.BTW, findings in Lakatos et al., 2008 suggest that pro-and counter-entrainment may provide a mechanism for aftenfional enhancement and suppression of input transmission from V1 layer 4  3 4) There is a fascinafing statement on p4: "At longer fimescales (> 60 ms lag)33, while aftenfion weakened pairwise dependencies, in agreement with previous findings38,39, we found a strengthening of unique dependencies."It is hard to extrapolate too far from this, but events separated by 60 ms could fall into opposite phases of an 8 Hz oscillafion.It would be nice to see some follow-up quanfificafion of strength of effect as a funcfion of lag.While it is likely beyond the scope of the present analysis, hopefully the authors will be able to incorporate analysis of field potenfial dynamics at some point.5) p6: "these behavioral fluctuafions are thought to arise from endogenous fluctuafions such as aftenfional sampling and arousal changes."Lakatos et al., (Nat.Neurosci.2016) provide a nice mechanisfic analysis of how state fluctuafions relate to input processing and behavior.6) given how important accurate layer assignment is for this paper, a clearer jusfificafion of the use of the CSD profile to assign layers would be helpful.I looked at the Nandy et al paper, and that was not very helpful on this point.

Reviewer #2 (Remarks to the Author):
This is an important topic conveyed by a good (however long) abstract.I have a few comments: -The introducfion is too short, essenfially only the first sentence.There should be a whole introducfion on the topic before delving into details, describing the literature, compefing hypotheses, the importance of the quesfion for general topics in neuroscience, etc.Right now, the arficle was wriften for a different, more specialized journal with another format.
-The arficle is too short and lacks some explanafions and intuifions for nearly everything.It's difficult and unpleasant to read for a neuroscienfist who does not work directly on this quesfion.It needs much work to correct this.We should be able to understand most of the arficle without looking at the Methods, with some intuifive explanafions.For example, can you give some intuifion about what is a "Dynamic Bayesian Network" and why it is an unbiased approach?Can you explain the results instead of just menfioning them and referring to the figures?Even if the arficle is a short communicafion, it should be more accessible to the broader scienfific community.
-Line 78, where is the task descripfion in the paper's main text?And of the behavior, etc.? -The abbreviafion "GLM" is menfioned as is without first explained.There is a lot of jargon specific to this topic that should be quickly explained.
-Where is the computafional model menfioned in the abstract?
Reviewer #3 (Remarks to the Author): The paper makes a great observafion that responses within V4 are modulated by aftenfion in a way that reduces dependencies selecfively.There are two types of dependencies between neurons.First are dependencies that are shared, possibly due to a common source, by these neurons.Another that is informafion that a neuron contains that is unique to it and not present in any other neuron.They show that aftenfion reduces the shared informafion reducing correlafions and improves the unique informafion neuron representafions which would improve object specific encoding.This interesfing finding would be much befter served if the authors consider the following issues with the manuscript: presentafion, making concepts more explicit, and describing their experimental techniques in more detail.I am aware that the short format might constrain these issues, but, currently the paper is unclear at a first reading, and to fully realize the potenfial of the findings it would be important for them to address them.The main issues in more detail below.
1.The first issue is providing sufficient background on a couple of fronts.On the neuroscience front, they should be more explicit about what is known about how aftenfion can modulate neuronal responses, and how this modulafion is helpful for funcfion.For instance, they menfion object recognifion in the abstract.Perhaps menfion the implicafions in discussion.Some context would then provide a befter grounding for the whole paper.Secondly, they should be more explicit about the two techniques that they used and in which part.They talk about PID as a mofivafion to understand unique and shared dependencies between source and target and also DBN as a way to decipher informafion flow.They should give some background about the use of these two techniques in neuroscience and describe briefly how it has helped.
2. The second issue relates to the first.They used a synthefic network to apply their techniques and demonstrate their applicability.They should describe in more detail where they used the PID technique and where they used the DBN technique.One has to sift through the methods and the figures to understand what they did.
3. An important methodological issue that is not clear to me is what the limitafions of these techniques are.Are there some instances where they fail to pick out the informafion flow or where shared and unique dependencies are not deciphered?The synthefic model presents an instance where it works.This point would be moot if the synthefic network is a replica of the real one, but, it is not so clear to me that it is as I think the corfical network is more complex (related point 5 below).Perhaps, the authors can show that this is a phenomenological model that in some limit captures the essenfials of the V4 network, and other connecfions are not central to their conclusion.Or, perhaps, PID and DBN are guaranteed to reveal the proper characterisfics of any network.They should in some way show that their techniques are viable, since the paper hinges on their techniques revealing informafion flow and dependencies.Could you provide a befter explanafion in legends.Also, some more explanafion on these results panels would also be helpful.Also, the posifioning of the panels is not intuifive.I understand you are trying to conserve space, but consider making it so that you can go from left to right or top to boftom.Not, left to right, and then switching to top to boftom.
5. One issue is with some of the explanafions in the methods secfion.They menfion a synthefic network and postulate different models with 2 neurons or 6 neurons.What is the reason behind their choosing specific configurafions?Is this likely to reflect the corfical network?If so, how will it scale with the actual numbers in cortex?6.More details in other places would also be helpful.What is the precise region that was recorded from?How many neurons?Details of animals: two as menfioned in methods.These details should be at least briefly menfioned in the main text.7.They make the point of unique and shared dependencies.How will modulafion by aftenfion help with functon(what they observe)?Some thoughts on this in the discussion secfion of the paper would give befter context to the findings.8.They make the point that they cannot comment on the connecfivity, which is understandable.But, they talk about input and superficial layers.Could they talk about connecfivity between these layers, and the inputs to the region and the outputs, and how the reducfion in correlafions would influence the output of V4?This goes more with providing more context for people who are not in this exact area and improving readability for the audience.9. Minor typos like line 60, capturing "the" unique dependency structure.

Line 100 and 124 instead of Same can state what it is again.
Last point to consider is that a more compact abstract would pack more punch.

Reviewer #4 (Remarks to the Author):
The key findings of this paper can be summarized as follows: The authors employed a technique called Parfial Informafion Decomposifion to disfinguish various interacfions both within and between regions in the visual cortex.This approach is crucial because it helps the authors disambiguate between redundant, synergisfic, union, and unique components of informafion across and within layers of the ventral visual hierarchy.It helps them address two hypotheses, namely, that spafial aftenfion (i) aids hierarchical computafions by enhancing the transfer of unique informafion across layers and/or (ii) decreases the redundancy of within layer informafion.Other methods don't effecfively isolate different components.
The authors introduced a new method termed MTwDBN for idenfifying dependencies between nodes in a graph.This method is a modificafion of Dynamic Bayesian Networks (DBN).Here, the stafisfical significance of edge weights is determined using a fime-shuffled esfimate.The authors assert that MTwDBN outperforms exisfing methods, especially when dealing with sparse data.
Using the above-menfioned techniques, the authors demonstrated the following outcomes: Enhancement of unique informafion across different layers Reducfion of redundant informafion within layers.

Increase of unique informafion within layers.
Notably, this increase is observed in projecfion neurons but not in inhibitory interneurons.
These findings are both promising and original.The paper highlights that the applicafion of the techniques developed here was essenfial in reaching these conclusions.However, one crifique of the paper pertains to the concise explanafions provided for findings 1 and 2.Although PID has been ufilized in a different context, and MTwDBN represents an extension of convenfional Dynamic Bayesian methods, these aspects receive limited aftenfion in the main body of the paper despite it being germane to the results that follow.Much of the detailed informafion is relegated to the supplementary methods secfion.While the final results (3a-d) are novel and engaging, a more comprehensive explanafion of points 1 and 2 within the main paper would be appreciated.Further, there is also no menfion of the caveats associated with using PID.This would be a valuable addifion to the paper.

Response to Reviewers
We greatly appreciate the insigh0ul comments and advice for improvements from all four reviewers.Below we provide detailed responses to each of the reviewers' comments and outline new analyses that are incorporated into a significantly revised and expanded manuscript.We hope that with this comprehensive revision, the editor and reviewers will find our manuscript suitable for publica?on at Nature Communica?ons.
Reviewer #1 (Remarks to the Author): Review of Das et al., (submi<ed to Nature CommunicaBons) This paper's stated aim is to invesBgate the neural mechanisms by which spaBal a<enBon aids hierarchical computaBons during object processing and recogniBon.Specifically, they contrast 2 mechanisms proposed by prior studies; 1) increase in the efficacy of unique informaBon directed from one encoding stage to another, and 2) an improvement in the sensory informaBon capacity of an encoding stage through a reducBon in shared fluctuaBons.They note that pairwise analyses have the limitaBon that they capture both unique and shared components of fluctuaBons, and thus do not differenBate between mechanisms.To test these proposals, the authors esBmated a<enBonal modulaBon of unique informaBon flow across and shared informaBon within the stages of the visual hierarchy in using the layers 4 and the superficial layers of macaque V4 as a proxy for stages.They used network-based staBsBcal modeling to measure staBsBcal dependencies indexing how middle and superficial corBcal layers uniquely drive each other's neural acBvity.They found that a<enBon strengthened unique dependencies between the input and superficial layers and then used a parBal informaBon decomposiBon framework to esBmate modulaBon of shared dependencies.This suggested that within-layer shared dependencies are reduced in broad spiking (~excitatory) populaBons, as well as an unheralded within-layer strengthening of unique dependencies.They then examined these modulaBon pa<erns across epochs of Bme in which hits/misses were more/less likely.Using their findings along with earlier theoreBcal proposiBons they propose a computaBonal model applicable to a<enBonal enhancement and endogenous fluctuaBons in task performance: "enhanced informaBon flow between and improved informaBon capacity within encoding stages." On the plus side, there is a lot to like about this paper.The deployment of a set of staBsBcal approaches that can differenBate between shared and unique dependences is a novel feature and a major strength of the paper.The first author's in-depth familiarity with the data set gained through parBcipaBon in the original data collecBon experiments is also enormously helpful to the paper.The informed applicaBon of computaBonal modeling to index the analyBc approach to some ground truth is another of the paper's strengths.Overall, the paper has the potenBal to be an excellent-outstanding contribuBon.
We thank the reviewer for their enthusias?cassessment of our study.
On the minus side, there are a few concerns, one quite broad, and several more specific, that the authors might address.The broad concern is mainly conceptual and has a number of facets.To begin with I'll idenBfy myself (Charlie Schroeder).

BROAD CONCERN:
There is a key division of perspecBve in the literature on a<enBon in non-human primates that shows up here in various forms but is not directly addressed by the authors.On the one hand, starBng with the seminal studies in mid 1980s by Desimone and colleagues (including Reynolds et al) and conBnuing with the studies of Maunsell and colleagues, invesBgators have taken the perspecBve that an essenBal component of a<ending is suppression of low frequency excitability fluctuaBons (aka oscillaBons) and possibly enhancement of higher frequency (e.g., gamma).On the other hand, there is gathering recogniBon that the lower frequencies persist during a<enBve states and are mechanisBc instruments in a<enBonal modulaBon of neuronal acBvity in the visual system.Relevant studies in both NHPs and humans include those of the Kastner group and the Knight group (cited here, refs 15-17) concerning the role of alpha/theta range oscillaBons in spaBal channel switching during sustained a<enBon), and other studies by Lakatos and colleagues (and others not cited here) showing that a<enBon uses low frequency (theta-delta range) entrainment to aid in selecBon of task relevant input at the expense of distractor input.The reason that this is important is that findings under the "low frequency suppression" framework generally entail random presentaBon of sBmuli, while those in the "low frequency entrainment" framework perspecBve entail rhythmic or quasi-rhythmic input presentaBon.Reynolds and colleagues (from whose lab the empirical data for this study were drawn) make a strong case (as may be obvious in the foregoing discussion) that low frequency dynamics are more "broad-band" than "narrowband" in character.I would agree with that.
Are these consideraBons relevant here?I would say yes for several reasons.First, it is likely that a<enBon in the real-world alternates between random and rhythmic (and perhaps mixed) modes of operaBon (e.g., Schroeder & Lakatos, 2009;Schroeder et al., 2010), parBcularly since real world vision requires acBve saccadic sampling of informaBon (Barczak et al., 2019); this is arguably the overt (motor) equivalent of covert spaBal a<enBon.Saccades occur quasi-rhythmically at rates of 2-5 Hz, which strongly entrains visual pathway acBvity at these (delta-theta) rates.Second, enhancement of informaBon flow across layers and across areas likely requires synchronizaBon of acBvity at whatever fluctuaBon/oscillaBon rate is dominant.Third, the very presence of fluctuaBons/oscillaBons, however bursty/transient and on whatever Bme scale (NeymoBn et al., 2022; Tal et al., 2022) raises a conundrum that the authors would need to address if their proposed computaBonal model is to have general relevance for the mechanisBc study of a<enBon: the high excitability phase is where acBon potenBal most olen occur, and synchronisaBon of high excitability states x layers and/or areas is one of the more obvious ways to enhance informaBon flow, HOWEVER, the high excitability phase is where one would expect "noise correlaBons" to be the strongest.This could mean, for e.g., that the low excitability phase is paradoxically the best state for transmission of "unique" informaBon (see point 4 below).In any case, I am missing in this paper is a consideraBon of the impact of neuronal dynamics, parBcularly in the lower (<30 Hz) frequencies.Given that as menBoned above, real world visual processing (in monkeys and humans) usually entails moving the eyes a lot, with the a<endant impact on neuronal dynamics, failure to fully consider dynamics and Bme may limit the generality of this otherwise fine work (again, see point 4).

Dr. Schroeder has raised several excellent points here regarding poten?al mechanisms of aHen?onal enhancement in the context of inter-stage communica?on within the hierarchical architecture of the ventral visual pathway. Especially helpful has been the hypothesis he has ar?culated underlying the neural mechanism of unique dependency enhancement between the input and superficial layers: namely, that the best state for the transmission of "unique" informa?on could be the phases of low neural excitability. To directly test this hypothesis, we conducted new analyses that we now report in our Results (ln 214-227; Figs 5f,g and S5):
"To test if optimal states that are associated with hit trials are also associated with a reduction in shared correlations among the projection neurons of the input layer, we estimated the probability of presentation of "successful" target stimuli and the probability of spiking of input layer broad spiking units, both as a function of the phase of the ongoing cortical activity.We estimated the generalized phase of the bandfiltered

local field potential signals in the input layer (see Methods), and calculated the probability of a 'hit'-causing target onset and of neuronal spikes at different phases (Fig 5f). We found a clear phase dependence of response onset of 'hit' targets (Fig 5g, top). These phases were also associated with a lower excitability of broad spiking cells (Fig 5g, bottom), suggesting that the improved performance in optimal states occurs during phases of lower than average spiking probability of putative excitatory neurons in the input layer. Interestingly, the excitability of superficial layer putative excitatory neurons, the primary candidates that project to downstream cortical areas in the ventral stream, was independent of the phase of the ongoing activity in the superficial layers of V4 (Fig S5)."
We have added a discussion of the implica?ons of these findings within the context of the above hypothesis (ln 266-274): "Addi?onally, we find that phases of the endogenous fluctua?ons that are associated with op?mal target presenta?on(resul?ng in hits) are also associated with reduced excitability of broad spiking neurons, especially in the input layer.Our findings are in agreement with previous reports of rhythmic shics of neural excitability and their entrainment to the stream of sensory inputs as key mechanisms of sensory selec?on 1-3 .Interpre?ng the fluctua?ons in excitability to be at least partly based on changing correla?ons due to fluctua?ons in shared inputs, our finding suggests an addi?onal mechanism through which weakened shared neural ac?vity fluctua?ons could improve behavioral outcome: a lowered excitability, which is associated with reliable encoding in the visual cortex 4 " We thank Dr. Schroeder again for this excellent sugges?on which we believe has complemented our causal model by providing a concrete mechanism of the transmission of unique informa?on across cor?cal layers.

MORE SPECIFIC CONCERNS:
1) It would be really helpful to define "unique informaBon" and "unique dependencies" early in the paper.

We agree and have incorporated this sugges?on in our rewriHen manuscript. Please see the updated Results sec?on (ln 82-90) and the corresponding revised figures (Fig 1a, Fig S1).
2) The term "fluctuaBons" seems to correspond to neuronal "dynamics" or "oscillaBons" but it would be be<er to clear this up early rather than leaving the reader puzzling over this.
We agree that it is necessary to qualify "fluctua?ons" to provide clarity in the text.The term was previously used to describe the dynamics of neural ac?vity, behavioral performance, or brain state in different parts of the manuscript.We have now made the necessary correc?ons in the revised manuscript.
3) p2, top: do "laminar" stages (here middle to upper layers of V4) really represent "cross-areal" stages?I'm prepared to buy that for the moment, but one problem here that laminar projecBon pa<erns from Layer 4 to Layers 2/3 are biased in favor of the "feedforward" (layer 4 à 3) projecBon, whereas corBcocorBcal projecBons tend more towards symmentry.It seems that the analyses here assume a ~ symmetrical 4 ßà 2/3 projecBon pa<ern.BTW, findings in Lakatos et al., 2008 suggest that pro-and counter-entrainment may provide a mechanism for a<enBonal enhancement and suppression of input transmission from V1 layer 4 à3 We agree that there are differences in the inter-laminar vs inter-areal connec?vity when considering the balance of feedforward/feedback projec?ons.The aspect of hierarchical architecture that we focus on in this study is a robust feedforward connec?vity mo?f -present in both inter-laminar and inter-areal circuits -that supports the downstream informa?on flow crucial for object recogni?on.However, the point raised by Dr. Schroeder is an important one and will be a focus of future queries that we discuss in the revision (how are dependency components modulated in feedforward vs. feedback direc?on? ln 276-289).The agreement of our findings (in a feedforward-dominated circuit) with previous studies showing enhanced communica?onthrough both correla?on-based analyses and causal manipula?on(electrical s?mula?on) in the more balanced V1->MT Dorsal pathway 5 suggests that it would be reasonable to assume similar mo?fs of aHen?onal modula?on at both inter-laminar and inter-areal levels.
4) There is a fascinaBng statement on p4: "At longer Bmescales (> 60 ms lag)33, while a<enBon weakened pairwise dependencies, in agreement with previous findings38,39, we found a strengthening of unique dependencies."It is hard to extrapolate too far from this, but events separated by 60 ms could fall into opposite phases of an 8 Hz oscillaBon.It would be nice to see some follow-up quanBficaBon of strength of effect as a funcBon of lag.While it is likely beyond the scope of the present analysis, hopefully the authors will be able to incorporate analysis of field potenBal dynamics at some point.
We have heeded this sugges?onand have added new results regarding field poten?al.Since we find strengthening of dependencies at mul?ple lags, we analyzed a broadband 5-40 Hz signal to examine the rela?ve phase rela?onship in a more unbiased manner.Our new analysis (Fig. 5f-g) of the broadband LFP signal does suggest op?mal phases of s?mulus presenta?onthat also correlate with phases of low excitability, in a cell-class specific way) (ln 213-227).Please also see our response to the main point above.
5) p6: "these behavioral fluctuaBons are thought to arise from endogenous fluctuaBons such as a<enBonal sampling and arousal changes."Lakatos et al., (Nat.Neurosci.2016) provide a nice mechanisBc analysis of how state fluctuaBons relate to input processing and behavior.
We thank Dr. Schroeder for bringing this per?nent study to our aHen?on.We now discuss it in our manuscript when we interpret our new result (Fig 5 f,g) in the rewriHen Discussion sec?on (ln (266-274).
6) given how important accurate layer assignment is for this paper, a clearer jusBficaBon of the use of the CSD profile to assign layers would be helpful.I looked at the Nandy et al paper, and that was not very helpful on this point.
We have now added text in the Methods sec?on to elaborate on the criteria used for assigning layer boundaries (ln 762-769).We have also added an example CSD profile marked up to illustrate our approach in a new main figure (Fig. 3).

Reviewer #2 (Remarks to the Author):
This is an important topic conveyed by a good (however long) abstract.
We thank the reviewer for enthusias?callysuppor?ng our manuscript.

I have a few comments:
-The introducBon is too short, essenBally only the first sentence.There should be a whole introducBon on the topic before delving into details, describing the literature, compeBng hypotheses, the importance of the quesBon for general topics in neuroscience, etc.Right now, the arBcle was wri<en for a different, more specialized journal with another format.
We completely agree with this assessment and have now extensively rewriHen and expanded the manuscript.
-The arBcle is too short and lacks some explanaBons and intuiBons for nearly everything.It's difficult and unpleasant to read for a neuroscienBst who does not work directly on this quesBon.It needs much work to correct this.We should be able to understand most of the arBcle without looking at the Methods, with some intuiBve explanaBons.For example, can you give some intuiBon about what is a "Dynamic Bayesian Network" and why it is an unbiased approach?Can you explain the results instead of just menBoning them and referring to the figures?Even if the arBcle is a short communicaBon, it should be more accessible to the broader scienBfic community.
We have amended these shortcomings by completely rewri?ngand expanding the manuscript.We now summarize our approach in the Results sec?on (ln 111-121) and provide intui?ons for the concepts with the help of the redesigned DBN schema?cs in the related figure (Fig. 2).Our expanded Results sec?on elaborates on each of our findings.Further, we have elaborated on the implica?ons of the Results, specifically the func?onal role of the modula?on of different components of dependencies in op?mal sensory states, in the revised Discussion sec?on.
-Line 78, where is the task descripBon in the paper's main text?And of the behavior, etc.?
We have updated the Results sec?on (ln 141-156) and added a new main figure (Fig. 3) to address this point.We have also expanded the Methods sec?on to provide further details (ln 737-753).
-The abbreviaBon "GLM" is menBoned as is without first explained.There is a lot of jargon specific to this topic that should be quickly explained.
We have corrected this and added text to summarize the GLM approach (ln 312-323).
-Where is the computaBonal model menBoned in the abstract?
We regret the confusion in the wording.We have rewriHen our abstract and now clearly refer to a conceptual model where needed in the manuscript.
Reviewer #3 (Remarks to the Author): The paper makes a great observaBon that responses within V4 are modulated by a<enBon in a way that reduces dependencies selecBvely.There are two types of dependencies between neurons.First are dependencies that are shared, possibly due to a common source, by these neurons.Another that is informaBon that a neuron contains that is unique to it and not present in any other neuron.They show that a<enBon reduces the shared informaBon reducing correlaBons and improves the unique informaBon neuron representaBons which would improve object specific encoding.
We appreciate the enthusias?creview of our study and the detailed sugges?ons for improvement of the manuscript.
This interesBng finding would be much be<er served if the authors consider the following issues with the manuscript: presentaBon, making concepts more explicit, and describing their experimental techniques in more detail.I am aware that the short format might constrain these issues, but, currently the paper is unclear at a first reading, and to fully realize the potenBal of the findings it would be important for them to address them.The main issues in more detail below.
1.The first issue is providing sufficient background on a couple of fronts.On the neuroscience front, they should be more explicit about what is known about how a<enBon can modulate neuronal responses, and how this modulaBon is helpful for funcBon.For instance, they menBon object recogniBon in the abstract.Perhaps menBon the implicaBons in discussion.Some context would then provide a be<er grounding for the whole paper.
Our rewriHen manuscript now addresses this by providing a background in the Introduc?on(ln 47-58) and implica?ons of the results in the Discussion sec?on (ln 231-274).
Secondly, they should be more explicit about the two techniques that they used and in which part.They talk about PID as a moBvaBon to understand unique and shared dependencies between source and target and also DBN as a way to decipher informaBon flow.They should give some background about the use of these two techniques in neuroscience and describe briefly how it has helped.
We reworked Figures 1 and 2 to break down the build-up of the concepts underlying our techniques.We also summarize our techniques in the Results sec?on (ln 82-88, 111-125) and elaborate on them in the Methods sec?on of the main text (ln 515-537, 572-606).
2. The second issue relates to the first.They used a syntheBc network to apply their techniques and demonstrate their applicability.They should describe in more detail where they used the PID technique and where they used the DBN technique.One has to sil through the methods and the figures to understand what they did.
We have fully rewriHen the main text, reworked Figures 1 and 2, and expanded the Methods to address these points.
3. An important methodological issue that is not clear to me is what the limitaBons of these techniques are.Are there some instances where they fail to pick out the informaBon flow or where shared and unique dependencies are not deciphered?The syntheBc model presents an instance where it works.This point would be moot if the syntheBc network is a replica of the real one, but, it is not so clear to me that it is as I think the corBcal network is more complex (related point 5 below).Perhaps, the authors can show that this is a phenomenological model that in some limit captures the essenBals of the V4 network, and other are not central to their conclusion.Or, perhaps, PID and DBN are guaranteed to reveal the proper characterisBcs of any network.They should in some way show that their techniques are viable, since the paper hinges on their techniques revealing informaBon flow and dependencies.
We scope the discussion of our results as follows: "The laminar network is considered a canonical circuit that cons?tutes a key computa?onal unit in the cortex.While anatomical connec?vity maps have iden?fied the key variables of this unit 6,7 , access to the func?onal connec?vity that determines the resul?ng computa?ons has remained elusive.Laminar recordings in awake animals transi?oning across behavioral states have allowed us to observe the neural variables that are expected to play a significant role in these computa?ons."(ln 231-236) We further discuss the caveats of our approach in the Discussion sec?on of the revised manuscript (ln 325-347).
Addi?onally, the s?muli used in our experiment were of a level of complexity comparable to the tuning of V4 neurons, hence feedback from higher areas was not expected to play an important role in the neural ac?vity in V4.The neural variable that was expected to have a key role in the local ac?vity is the input from regions (e.g.frontal eye fields) that signal the aHen?ve state.Since this was the independent variable that was explicitly manipulated in our experiment, we compared how this variable modulates the dependency structure in the local V4 network.We have reorganized Figure 2, and updated its cap?on and expanded the Methods sec?on (Mul?-?me lag Weighted Dynamic Bayesian Network (MTwDBN) Analysis Pipeline; ln 572-606) to address these issues in a comprehensive manner.
Next, looking at Figure 3, at least to me, it wasn't clear what the bars were in e-k.Could you provide a be<er explanaBon in legends.Also, some more explanaBon on these results panels would also be helpful.
We have added helpful schema?cs to figure panels a/g and explanatory text in the cap?on for this figure (now Fig. 4).Addi?onally, we have rewriHen the Results sec?on related to this figure to further clarify the results shown in Fig. 4 (ln 158-194).
Also, the posiBoning of the panels is not intuiBve.I understand you are trying to conserve space, but consider making it so that you can go from lel to right or top to bo<om.Not, lel to right, and then switching to top to bo<om.
We have now fixed this issue in the newly organized figure (now Fig. 4).
5. One issue is with some of the explanaBons in the methods secBon.They menBon a syntheBc network and postulate different models with 2 neurons or 6 neurons.What is the reason behind their choosing specific configuraBons?Is this likely to reflect the corBcal network?If so, how will it with the actual numbers in cortex?
We used the synthe?cnetworks to inves?gate if (a) edge weight reflects dependency strength and (b) how well our method discovers the dependencies in a recurrent mul?-variate network for which the ground truth is known.The 2-network configura?onwas used to inves?gate the rela?onship of "edge weight" in our MTwDBN graphs to the underlying dependency strength.The 6-popula?on network was built to inves?gate how the method performs in a mul?-variable network with recurrent connec?vity.Since the rela?onship between dependency strength and synap?c connec?on strength in this kind of network is complex, we used two separate configura?ons to inves?gate these two points.6.More details in other places would also be helpful.What is the precise region that was recorded from?How many neurons?Details of animals: two as menBoned in methods.These details should be at least briefly menBoned in the main text.
We summarize these details in the main text (ln 143-147).Addi?onally, we have moved all the experimental details to the Methods sec?on of main text and added a new main figure (Figure 3) to illustrate the experimental and recoding paradigm.
7. They make the point of unique and shared dependencies.How will modulaBon by a<enBon help with functon(what they observe)?Some thoughts on this in the discussion secBon of the paper would give be<er context to the findings.
We agree and now elaborate on this in the new Discussion sec?on (ln 231-274).
8. They make the point that they cannot comment on the connecBvity, which is understandable.But, they talk about input and superficial layers.Could they talk about connecBvity between these layers, and the inputs to the region and the outputs, and how the reducBon in correlaBons would influence the output of V4?This goes more with providing more context for people who are not in this exact area and improving readability for the audience.
Point well taken.We now provide a brief summary of these points in the Introduc?on(ln 60-78) and extensively explore them in Discussion (ln 231-274).9. Minor typos like line 60, capturing "the" unique dependency structure.
We fixed this (now ln 114).We have explicitly stated the cap?ons in the revised figure (now Fig. 4).
Last point to consider is that a more compact abstract would pack more punch.

4 .
The figures are a liftle confusing.For instance, consider Figure 2.You have two paths in A, from resample and shuffle.It is not clear from the figure what is happening.And, there is very liftle explanafion in the legends.Again, you have to dig through the methods to get some context and understand.The figures should provide a stand-alone explanafion.Next, looking at Figure 3, at least to me, it wasn't clear what the bars were in e-k.

4 .
The figures are a li<le confusing.For instance, consider Figure2.You have two paths in A, from resample and shuffle.It is not clear from the figure what is happening.And, there is very li<le explanaBon in the legends.Again, you have to dig through the methods to get some context and understand.The figures should provide a stand-alone explanaBon.
10. Line 100 and 124 instead of Same can state what it is again.